MATLAB and Simulink Robotics Arena: From Data to Model

With a focus on student robotics competitions, Connell D'Souza and Kris Fedorenko show you how to get started with black box modeling. You will be exposed to basic modeling concepts, and see a demonstration of the system identification process on real-life data from Blue Robotics. Having a model allows you to design and test a controller as well as model a larger system—important steps for preparing your robot for a competition. You can find all the data used in this video on MATLAB Central’s File Exchange.

The System Identification app enables you to perform all stages of modeling such as importing and preprocessing the data, trying out different model structures, and evaluating the resulting models. Two datasets of input and output data for a T200 Blue Robotics Thruster are used to demonstrate the modeling process. Connell and Kris show how to process the data by removing means and filtering out noise. Several models are then created using simple linear model structures like state space model and transfer function, illustrating that modeling is an iterative process. You will also learn how to use validation data to evaluate your models.

After this video, you should be able to create a reasonable model for your own hardware component. As Connell and Kris underline, you would need to collect good-quality input and output data for both estimation and validation, start with simpler model structures, and keep iterating until you achieve good results. You might find the following links helpful:

Introduction to Robotic Systems
Meet MATLAB and Simulink Robotics Arena team members, Sebastian Castro and Connell D’Souza, as they discuss designing a robotic system and the support provided to robotics student competition teams.

Introduction to Contact Modeling, Part 1
Sebastian Castro and Ed Marquez Brunal introduce the fundamentals of mechanical contact modeling and simulation with Simulink, as well as show examples for automotive and robotics applications.

Direction of Arrival with MATLAB
Stephen Cronin from the Robotics Association at Embry-Riddle Aeronautical University demonstrates how to detect the direction of arrival of an underwater acoustic signal using MATLAB.

Walking Robots, Part 2: Actuation and Control
Join Sebastian Castro as he shows you how you can use Simulink and the Simscape product family to connect a walking robot model to detailed actuator models with motion planning and control algorithms.

Real-Time Beat Tracking Challenge
Jeremy Bell, Angus Keatinge, and James Wagner of The University of New South Wales (UNSW Sydney) discuss their team’s winning entry to the IEEE Signal Processing Cup 2017.

Deploying Algorithms to ROS
Join Sebastian Castro and Pulkit Kapur as they show how automatic code generation tools can help you deploy algorithms developed in MATLAB and Simulink to run in the Robot Operating System (ROS).

Building Interactive Design Tools
Build interactive tools design tools to reduce development time. Zachary Leitzau from Embry-Riddle Aeronautical University demonstrates the use of a self-built app to help design a model airplane.

Simulating Quadcopter Missions
Simulation is a great way to test and tune control algorithms for quadcopters. Julien Cassette talks about using Simulink, Robotics Operating System (ROS), and Gazebo to simulate quadcopter missions from student competitions.

Optimizing Airframe Sizing
Follow Joshua Williams from Cornell University Unmanned Air Systems (CUAir) as he demonstrates the use of a genetic algorithm to optimize airframe sizing for model airplanes.

Designing Distributed Systems with ROS
Join Sebastian Castro and Connell D’Souza as they discuss techniques in Simulink to design and deploy multirate and multiplatform robotics algorithms with the Robot Operating System (ROS).

Designing Robot Manipulator Algorithms
Accelerate the design of robot manipulator algorithms by using the Robotics Systems Toolbox functionality and integrating robot models with simulation tools to program and test manipulation tasks.

Introduction to Filter Design
Join Mark Schwab and Connell D'Souza as they demonstrate the use of the Filter Designer app and interactively design filters for digital signal processing that can be implemented in MATLAB or Simulink.

From Data to Model
Create a model for a piece of hardware from input and output data using the System Identification app. Connell D'Souza and Kris Fedorenko explain the workflow from data gathering to model evaluation.

Ball Tracking with a Desktop Computer
In this session you’ll learn how to deploy MATLAB® and Simulink® onto a desktop computer for the purpose of controlling an Unmanned Vehicle System in student competitions.